import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
__a    :	List[Any]         =    (7_2_0, 1_2_8_0)  # Height, Width
__a    :	List[str]         =    (0.4, 0.6)  # if height or width lower than this scale, drop it.
__a    :	Dict         =    1 / 1_0_0
__a    :	Tuple         =    """"""
__a    :	Optional[int]         =    """"""
__a    :	List[str]         =    """"""
__a    :	Optional[int]         =    2_5_0





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase     ,	__lowercase					     =				get_dataset(lowercase				,     lowercase				)
				for index in range(lowercase				):
								__lowercase					     =				random.sample(range(len(lowercase				)				)				,     4				)
								__lowercase     ,	__lowercase     ,	__lowercase					     =				update_image_and_anno(
								    lowercase				,     lowercase				,     lowercase				,     lowercase				,     lowercase				,     filter_scale=lowercase				,     )

								# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
								__lowercase					     =				random_chars(32				)
								__lowercase					     =				path.split(os.sep				)[-1].rsplit('''.'''				,     1				)[0]
								__lowercase					     =				F"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
								cva.imwrite(F"{file_root}.jpg"				,     lowercase				,     [cva.IMWRITE_JPEG_QUALITY, 85]				)
								print(F"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"				)
								__lowercase					     =				[]
								for anno in new_annos:
												__lowercase					     =				anno[3] - anno[1]
												__lowercase					     =				anno[4] - anno[2]
												__lowercase					     =				anno[1] + width / 2
												__lowercase					     =				anno[2] + height / 2
												__lowercase					     =				F"{anno[0]} {x_center} {y_center} {width} {height}"
												annos_list.append(lowercase				)
								with open(F"{file_root}.txt"				,     '''w'''				) as outfile:
												outfile.write('''\n'''.join(line for line in annos_list				)				)





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				[]
				__lowercase					     =				[]
				for label_file in glob.glob(os.path.join(lowercase				,     '''*.txt'''				)				):
								__lowercase					     =				label_file.split(os.sep				)[-1].rsplit('''.'''				,     1				)[0]
								with open(lowercase				) as in_file:
												__lowercase					     =				in_file.readlines()
								__lowercase					     =				os.path.join(lowercase				,     F"{label_name}.jpg"				)

								__lowercase					     =				[]
								for obj_list in obj_lists:
												__lowercase					     =				obj_list.rstrip('''\n'''				).split(''' '''				)
												__lowercase					     =				float(obj[1]				) - float(obj[3]				) / 2
												__lowercase					     =				float(obj[2]				) - float(obj[4]				) / 2
												__lowercase					     =				float(obj[1]				) + float(obj[3]				) / 2
												__lowercase					     =				float(obj[2]				) + float(obj[4]				) / 2

												boxes.append([int(obj[0]				), xmin, ymin, xmax, ymax]				)
								if not boxes:
												continue
								img_paths.append(lowercase				)
								labels.append(lowercase				)
				return img_paths, labels





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				,     lowercase				,     lowercase				,     lowercase = 0.0				,     ):







				"""simple docstring"""



				__lowercase					     =				np.zeros([output_size[0], output_size[1], 3]				,     dtype=np.uinta				)
				__lowercase					     =				scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
				__lowercase					     =				scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
				__lowercase					     =				int(scale_x * output_size[1]				)
				__lowercase					     =				int(scale_y * output_size[0]				)

				__lowercase					     =				[]
				__lowercase					     =				[]
				for i, index in enumerate(lowercase				):
								__lowercase					     =				all_img_list[index]
								path_list.append(lowercase				)
								__lowercase					     =				all_annos[index]
								__lowercase					     =				cva.imread(lowercase				)
								if i == 0:  # top-left
												__lowercase					     =				cva.resize(lowercase				,     (divid_point_x, divid_point_y)				)
												__lowercase					     =				img
												for bbox in img_annos:
																__lowercase					     =				bbox[1] * scale_x
																__lowercase					     =				bbox[2] * scale_y
																__lowercase					     =				bbox[3] * scale_x
																__lowercase					     =				bbox[4] * scale_y
																new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
								elif i == 1:  # top-right
												__lowercase					     =				cva.resize(lowercase				,     (output_size[1] - divid_point_x, divid_point_y)				)
												__lowercase					     =				img
												for bbox in img_annos:
																__lowercase					     =				scale_x + bbox[1] * (1 - scale_x)
																__lowercase					     =				bbox[2] * scale_y
																__lowercase					     =				scale_x + bbox[3] * (1 - scale_x)
																__lowercase					     =				bbox[4] * scale_y
																new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
								elif i == 2:  # bottom-left
												__lowercase					     =				cva.resize(lowercase				,     (divid_point_x, output_size[0] - divid_point_y)				)
												__lowercase					     =				img
												for bbox in img_annos:
																__lowercase					     =				bbox[1] * scale_x
																__lowercase					     =				scale_y + bbox[2] * (1 - scale_y)
																__lowercase					     =				bbox[3] * scale_x
																__lowercase					     =				scale_y + bbox[4] * (1 - scale_y)
																new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
								else:  # bottom-right
												__lowercase					     =				cva.resize(
												    lowercase				,     (output_size[1] - divid_point_x, output_size[0] - divid_point_y)				)
												__lowercase					     =				img
												for bbox in img_annos:
																__lowercase					     =				scale_x + bbox[1] * (1 - scale_x)
																__lowercase					     =				scale_y + bbox[2] * (1 - scale_y)
																__lowercase					     =				scale_x + bbox[3] * (1 - scale_x)
																__lowercase					     =				scale_y + bbox[4] * (1 - scale_y)
																new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)

    # Remove bounding box small than scale of filter
				if filter_scale > 0:
								__lowercase					     =				[
								    anno
								    for anno in new_anno
								    if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
								]

				return output_img, new_anno, path_list[0]





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				assert number_char > 1, "The number of character should greater than 1"
				__lowercase					     =				ascii_lowercase + digits
				return "".join(random.choice(lowercase				) for _ in range(lowercase				)				)


if __name__ == "__main__":
		main()
		print("""DONE ✅""")
import datasets


__a    :	Optional[Any]         =    """\
@InProceedings{conneau2018xnli,
  author = \"Conneau, Alexis
                 and Rinott, Ruty
                 and Lample, Guillaume
                 and Williams, Adina
                 and Bowman, Samuel R.
                 and Schwenk, Holger
                 and Stoyanov, Veselin\",
  title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",
  booktitle = \"Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing\",
  year = \"2018\",
  publisher = \"Association for Computational Linguistics\",
  location = \"Brussels, Belgium\",
}
"""

__a    :	Optional[Any]         =    """\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
"""

__a    :	str         =    """
Computes XNLI score which is just simple accuracy.
Args:
    predictions: Predicted labels.
    references: Ground truth labels.
Returns:
    'accuracy': accuracy
Examples:

    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> xnli_metric = datasets.load_metric(\"xnli\")
    >>> results = xnli_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}
"""





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				return (preds == labels).mean()




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,_KWARGS_DESCRIPTION					)
class   _UpperCamelCase (		datasets.Metric					):


			"""simple docstring"""






			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   str:

							'''simple docstring'''


							return datasets.MetricInfo(
							    description=_DESCRIPTION ,  citation=_CITATION ,  inputs_description=_KWARGS_DESCRIPTION ,  features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32'''		),
							            '''references''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32'''		),
							        }		) ,  codebase_urls=[] ,  reference_urls=[] ,  format='''numpy''' ,  )





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Dict:

							'''simple docstring'''


							return {"accuracy": simple_accuracy(lowerCAmelCase__ ,  lowerCAmelCase__		)}